Characterization of Txndc5 as a Biomarker of Humoral Immunity to Respiratory Syncytial Virus Infections in Infants in Kilifi County Kenya

Abstract

Respiratory syncytial virus (RSV) is the single largest cause of severe respiratory illness in infants and children globally. Worldwide, RSV pneumonia is the second biggest cause of post neonatal infant mortality, after malaria, causing an estimated 137,000 deaths/year (6.7% of all deaths in infants). There are currently no licensed vaccines against RSV infection as well as specific treatments for RSV pneumonia. The immunological correlates of severe disease following primary or repeat infection remain poorly understood. About 2% of the annual birth cohort is at risk of severe and potentially life-threatening RSV pneumonia. A substantial proportion of these severely ill infants remain at risk of acquiring similarly severe secondary infections. This risk of severe secondary disease can potentially be predicted by identifying host immune mechanisms that are predictive of protective immunity post primary infection. A potential avenue for identifying these protective immunity correlates is through the identification of host-specific biomarkers at the acute stage of primary infection that correlate with the magnitude of the ensuing protective immune response. Transcriptional biomarkers that are predictive of the magnitude of the virus-specific antibody response following exposure to RSV antigens would be an ideal way of profiling this secondary disease risk. In this study, the identification of potential predictors of immune response to RSV antigens was done in a vaccine trial. Gene expression levels of 47,231 probes targeting >25,000 annotated genes were assayed in whole blood using the Illumina Human HT-12 expression microarray bead chip. Expression levels for each probe at day 7 post-vaccination were compared to expression levels at day 0 (pre-vaccination) using a t-test corrected for multiple testing using a false discovery rate (FDR) of < 0.05 while serum antibody responses to vaccination were determined using ELISAs. Determination of the correlation between the expression levels of these genes and RSV-specific responses to vaccination was done using linear regression. From this analysis, six genes were upregulated above an arbitrary 2-fold threshold post-vaccination; TMPO, CD38, PRKACB, TXNDC5, LOC100131845 and LOC401845. The most strongly correlated with antibody responses to vaccination were CD38 (R2=0.61, p<0.0001) and TXNDC5 (R2=0.54, p<0.0001). TXNDC5 was selected for further validation as a predictor of antibody responses to RSV in natural infection in infants. Validation of TXNDC5 as a predictor of antibody responses to natural infection in infants was therefore done by determining the correlation between the expression levels of TXNDC5 in peripheral blood mononuclear cells collected at the acute stage of RSV infection with the magnitude of the RSV-specific antibody response in convalescence. The expression levels of TXNDC5 were assayed using quantitative Real time polymerase chain reaction (qRT-PCR). Total, RSV-specific and neutralizing antibodies were determined using enzyme linked immunosorbent assays (ELISA) assays and plaque reduction neutralization assays, respectively. Data was analyzed using STATA (v 12) and Graphpad Prism. The difference between the antibody levels at different time points (acute and convalescence) was compared using Wilcoxon test while spearman correlation was used to compare TXNDC5 expression levels with antibody responses to RSV infection. There was a significant difference in the acute convalescent antibody response (p<0.05). A significant positive correlation was observed between TXNDC5 copy numbers and the fold change neutralizing antibody titers (Rho=0.7, p=0.007) suggesting that TXNDC5 is a predictor of functional antibody response to RSV infection in infants. This study therefore provides preliminary evidence of an acute transcriptional biomarker that is moderately predictive of the functional antibody response to RSV infection. This forms the basis for further research in the identification and use of biomarkers in the prediction of later adaptive immune responses in natural infection and hence in disease risk prediction.